Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling

doi:10.3390/books978-3-03842-327-0

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Keywords

rainfall forecasting; flow forecasting; grey theory; geomorphology-based runoff model; flow updating algorithm; Grid-Xinanjiang model; scale effect; scale-invariant hydrologic response; subgrid parameterization; areal rainfall; conjugate gradient; Kohonen self-organizing feature map; Levenberg-Marquardt; multilayer perceptron; quickprop; rainfall disaggregation; artificial neural network; parameter calibration; hydrology; optimization; daily runoff forecasting; data preprocessing; linear perturbation model; singular spectrum analysis; artificial neural network; quantum-behaved particle swarm optimization (QPSO); daily runoff; reservoir forecasting; artificial neural network; hybrid forecast; monthly inflow; reservoir; forecast; artificial neural networks; support vector machine; genetic algorithm; hybrid method; SHP; power production; prediction; correlation analysis; streamflow forecasting; neural networks; support vector machine; enforced learning strategy; quantitative precipitation estimate; WRF model; RTTOV model; prior probability distribution function; conditional PDF; posterior PDF; accumulated total reservoir inflow; long lead-time hydrograph prediction; coupled heuristic inputs; real-time recurrent learning neural network; adaptive network-based fuzzy inference system; calibration sites; entropy; genetic algorithm; hydrological modeling; hydrological model; pre-storm soil moisture; runoff prediction; variable initial abstraction; runoff; invariance; non-linear model; recession parameter; symmetry; rainfall-runoff; meta-heuristic; data-driven; modeling; flood; prediction

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